Most of the robotics research involves the extensive use of ROS and Gazebo environments which are convenient
but they are not ideal platform for implementing learning algorithms especially reinforcement learning. To alleviate these problems, we are open sourcing our gym environment. We hope that this will act as a testbed for active localization research. Once trained on our simulator, the policies and the likelihood model learned are directly transferable to real robot.
We have also integrated the environment with various state of the art RL algorithms [1]} like PPO [2]}, A2C[3]}, ACKTR [4]}.
